A stochastic model and algorithms for determining efficient time–cost tradeoffs for a project activity

Abstract

We consider a stochastic model for the time-cost tradeoffs of an activity. In this model the activity can be undertaken by using several different resources, and the resource in use may change according to the way the activity is evolving. We present two algorithms for identifying strategies that are in a predefined neighborhood of the efficient set: one of them is based on a tree structure and the other is based on dynamic programming. Both algorithms take advantage of some mathematical properties of the model in order to reduce their running time and memory requirements. We present the results of some computational tests, as well as an application example. We conclude that the dynamic programming algorithm performs quite well, although it is sometimes necessary to adjust the parameters related to the neighborhood of the efficient set to be able to have reasonable running times.